Harmonizing the Electricity Sectors across North America: Recommendations and Action Items from Two RFF/US Department of Energy Workshops
Bibliographic record
Abstract
To address a number of the recommendations included in the US Department of Energy’s (DOE’s) Quadrennial Energy Review (QER), Resources for the Future—in concert with DOE, two partners (the International Institute for Sustainable Development and Instituto Tecnológico Autónomo de México) and two host institutions (Boise State University and the University of New Mexico)—held two workshops in October 2015, looking at the electricity sectors in the United States, Canada, and Mexico. The workshops had several purposes. First, to identify gaps, best practices, and inconsistencies with regulations and electricity system planning across the three large North American countries; second, to inform the creation of legal, regulatory, and policy roadmaps for harmonizing regulations and planning; and third, to bring together individuals who can help implement greater harmonization, and also others who can offer helpful input. The two workshops examined policies, regulations, and planning associated with the electricity sector, and within this sector, environmental regulations (for air pollution, greenhouse gases, and renewables), and regulations and processes associated with the operation and planning of the electricity system, including generation and transmission. This paper summarizes recommendations and observations of workshop participants. The recommendations include action items for DOE, other government agencies in all three countries, research groups, academics, stakeholders, and others, to move toward greater harmonization of policy and planning affecting the electricity system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".